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宋杰 等:基于深度学习的数字病理图像分割综述与展望 1457

[138] Zhang XF, Dou H, Ju T, Xu J, Zhang ST. Fusing heterogeneous features from stacked sparse autoencoder for histopathological
image analysis. IEEE Journal of Biomedical and Health Informatics, 2016,20(5):1377−1383.
[139] Gogna A, Majumdar A, Ward R. Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of
biomedical signals. IEEE Trans. on Biomedical Engineering, 2017,64(9):2196−2205.
[140] Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su ZZ, Du DL, Huang C, Torr PHS. Conditional random fields as recurrent
neural networks. In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2015. 1529−1537.
[141] Yang QS, Yan PK, Zhang YB, Yu, HY, Shi YY, Mou XQ, Kalra MK, Zhang Y, Sun L, Wang G. Low-dose CT image denoising
using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. on Medical Imaging, 2018,
37(6):1348−1357.
[142] Varghese A, Mohammed SKP, Sai Saketh C, Ganapathy K. Generative adversarial networks for brain lesion detection. In: Proc.
of the Medical Imaging 2017: Image Processing. 2017. [doi: 10.1117/12.2254487]
[143] Chen XR, Konukoglu E. Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv
preprint arXiv:1806.04972, 2018.
[144] Dou Q, Ouyang C, Chen C, Chen H, Heng PA. Unsupervised cross-modality domain adaptation of convnets for biomedical image
segmentations with adversarial loss. In: Proc. of the Int’l Joint Conf. on Artificial Intelligence. 2018. 691−697.
[145] Bokhorst JM, Pinckaers H, van Zwam P, Nagtegaal I, van der Laak J, Ciompi F. Learning from sparsely annotated data for
semantic segmentation in histopathology images. In: Proc. of the Int’l Conf. on Medical Imaging with Deep Learning. 2019.
84−91.
[146] Shan HM, Zhang Y, Yang QS, Kruger U, Kalra MK, Sun L, Cong WX, Wang G. 3-D convolutional encoder-decoder network for
low-dose CT via transfer learning from a 2-D trained network. IEEE Trans. on Medical Imaging, 2018,37(6):1522−1534.
[147] Jaccard P. The distribution of the flora in the alpine zone. New Phytologist, 1912,11(2):37−50.
[148] Dice LR. Measures of the amount of ecologic association between species. Ecology, 1945,26(3):297−302.
[149] Song J, Xiao L, Lian ZC. Boundary-to-marker evidence-controlled segmentation and MDL-based contour inference for
overlapping nuclei. IEEE Journal of Biomedical and Health Informatics, 2017,21(2):451−464.
[150] Dai JF, He KM, Sun J. BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In:
Proc. of the IEEE Int’l Conf. on Computer Vision. 2015. 1635−1643.
[151] Lin GS, Shen CH, van den Hengel A, Reid I. Efficient piecewise training of deep structured models for semantic segmentation. In:
Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. 2016. 3194−3203.
[152] Liu ZW, Li XX, Luo P, Loy CC, Tang XO. Semantic image segmentation via deep parsing network. In: Proc. of the IEEE Int’l
Conf. on Computer Vision. 2015. 1377−1385.
[153] Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic image segmentation with deep convolutional
nets, atrous convolution, and fully connected crfs. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017,40(4):
834−848.
[154] Peng CX, Zhang XY, Yu G, Luo GM, Sun J. Large kernel matters—Improve semantic segmentation by global convolutional
network. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. 2017. 4353−4361.
[155] Lin GS, Milan A, Shen CH, Reid I. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In:
Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. 2017. 1925−1934.
[156] Wu ZF, Shen CH, van den Hengel A. Wider or deeper: Revisiting the ResNet model for visual recognition. Pattern Recognition,
2019,90:119−133.
[157] Yuan YH, Chen XL, Wang JD. Object-contextual representations for semantic segmentation. arXiv preprint arXiv:1909.11065,
2019.
[158] Zhao HS, Shi JP, Qi XJ, Wang XG, Jia JY. Pyramid scene parsing network. In: Proc. of the IEEE Computer Society Conf. on
Computer Vision and Pattern Recognition. 2017. 2881−2890.
[159] Chen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint
arXiv:1706.05587, 2017.